改进的基于邻接树的贝叶斯网络推理算法  

Refined Junction-Tree-Based Algorithm for Reasoning in Bayesian Network

作　　者：胡春玲[1,2] 胡学钢[1] 姚宏亮[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009 [2]合肥学院网络与智能信息处理重点实验室,合肥230601

出　　处：《模式识别与人工智能》2011年第6期846-855,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60975034);国家自然科学基金海外及港澳学者合作研究基金项目(No.60828005);安徽省教育厅自然科学基金项目(No.KJ2010B177)资助

摘　　要：分析比较Shafer-Shenoy结构和Hugin结构两种经典的基于邻接树的贝叶斯网络推理算法.针对Hugin算法在推理分析领域的局限性,通过在Hugin算法的消息传播过程中引入零因子标志位和零因子处理机制,提出一种Hugin算法的改进算法R-Hugin.该算法具有良好的推理分析性能,并从理论和实验两个方面证明R-Hugin算法的正确性和有效性.Two classical junction-tree-based algorithms for reasoning in Bayesian network, Shafer-Shenoy architecture and Hugin architecture, are analyzed and compared. For the limitation of the Hugin algorithm in the reasoning analysis, a refined Hugin algorithm, R-Hugin, is proposed, which introduces the zero-factor flag and zero-factor processing mechanism in the message propagation process of the Hugin algorithm. R-Hugin algorithm has good reasoning and analyzing performance. Meanwhile, the correctness and efficiency of the R-Hugin algorithm are validated by theory and experiments.

关 键 词：贝叶斯网络 贝叶斯分析 推理 邻接树 零因子 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]